Performing Department
(N/A)
Non Technical Summary
The complex interactions of diet, microbiome, and the immune system play an important role in the occurrence of intestinal diseases and the degree of intestinal inflammation.It is well evidenced that dietary fiber can directly intervene with gut microbiota population, thus affecting host heath, which includes pathogen inhibition, inflammation modulation, and cancer prevention. The gut function will further influence host learning, memory, mood, and neural function through the microbiota-gut-brain (MGB) axis. Current dietary fiber recommendations are often limited and conflicting, failing to provide specific types and doses in the treatment of gastrointestinal disorders. The daily dietary fiber intake among underrepresented groups is far below the recommended amount. There is also an inconsistency in defining and classifying dietary fiber categories in literature.In recent years research on gut microbiome are attracting increasing attention due to the health benefits associated with gut microbiome. The gut microbiome is considered the "invisible organ" or "second brain" since it is related not only to the health of other organs but also to the neuro system. Dietary fiber, the major feeding substrate for gut microbes, carries a vital role to regulate the microflora. Gut microbiota is determined by many factors such as genetics, culture, geography, dietary history, etc.Dietary fiber is well proven as an effective intervention to modulate gut microbiome.The vast range of dietary fiber and its complex nature make it a challenging task to investigate the correlation between dietary fiber type and gut microbiome. Therefore, in the current project, dietary fiber will be classified into three major groups according to their degree of polymerization, monosaccharide composition, and linkage pattern, namely oligosaccharides, homopolysaccharides, and heteropolysaccharides.Correlating the microbial responses to the molecular linkage pattern and monosaccharide composition can help identify the specific fiber type thus linking the beneficial dietary fiber to its common food source. This will dramatically change the current status of the nutrition guideline on dietary fiber, e.g. 25g dietary fiber/per day, and dietary fiber sources and types will eventually be added to the guideline.?This project aims to develop a systematic approach to study the correlation of dietary fiber type and gut microbiome, as well as compare the obtained results with human data using a community cohort study among underrepresented populations.
Animal Health Component
50%
Research Effort Categories
Basic
50%
Applied
50%
Developmental
(N/A)
Goals / Objectives
This project will develop a new research module for understanding the correlation between dietary fiber type and gut microbiome and provide more specific evidence for categorized dietary fibers and their corresponding gut microbiota profile. This project will enhance the research capacity of 1890 institutions with novel ideas and research capacity to resolve emerging issues and equip our minority students with advanced research skills and knowledge to become highly competitive in the rapidly changing world.This Food and Nutrition research is among the top priorities at Tennessee State University's (TSU) College of Agriculture. Five major goals will be achieved:1. Evaluation of prebiotic effect of selected dietary fibers in categories of oligosaccharides, homopolysaccharides, and heteropolysaccharides using in vitro methods;2. Study on the correlation of dietary fiber type and gut microbiome using in vivo modules;3. Pre-clinical investigation of the correlation between different types of dietary fiber from foods and gut microbiome among underrepresented populations in the Southern Community Cohort Study;4. Promote the precise recommendation of dietary fiber guidelines;5. Build the research capacity in the new Department of Food and Animal Sciences at TSU by collaborating with scientists from nationally and internationally recognized institutions and industrial stakeholders.
Project Methods
Objective 1.Evaluate the prebiotic effect of selected oligosaccharides, homopolysaccharides, and heteropolysaccharides usingin vitromethod;Task 1.1. Measuring promotion of probiotic proliferation:The promotion of probiotic proliferation will be assessed using the method reported by Huang et al. (2019).Task1.2. Viscosity measurement of polysaccharide samples:Viscosity of polysaccharide solutions will be measured on a strain-controlled ARES G2 rheometer (TA Instruments, New Castle, DE, USA) using a cone-and-plate geometry (4 /50 mm) with a shear rate from 0.01 to 100 1/s at 25°C29.Task 1.3. in vitro fecal fermentation:Stool samples from five healthy individuals will be purchased from Medix Biochemica USA Inc. (St. Louis, MO, USA) and will be stored at − 80°C until further use. Stool samples will be thawed in an anaerobic chamber. The supernatant and the dietary fiber solutions (will be mixed with the culture medium. Supernatants will be used for SCFA analysis, and pellets will be used for microbiome analysis.Task. 1.4. SCFA analysis: Supernatant will be subjected to GC analysis with standards including acetate, propionate, butyrate, valerate, isobutyrate, and isovalerate.Task. 1.5. Molecular weight determination before and after fecal fermentation:Molecular weight distribution of polysaccharides before and after fermentation will be determined using Size Exclusion Chromatography (SEC) coupled with the triple detector module (SEC-MALS 20; Malvern Instruments, Westborough, MA, USA).Task1.6. Microbiome analysis:Approximately 150 mg of the sample will be used for DNA isolation by Zymo fecal DNA miniprep (Zymo Research, Irvine, CA, USA) in accordance with the manufacturer's instructions. The Quantitative Insights into Microbial Ecology (QIIME) version 1.9.1 will be used to analyze the 16S rRNA sequencing data generated from three replicate trial samples.Objective 2.Study on the correlation of dietary fiber type and gut microbiome on miceTask2.1. Gut microbiome analysisusing high-throughput16S rRNA amplicon sequencing:Feces of all groups of mice will be used to run the 16S rRNA sequence. Microbial genomic DNA will be extracted from fecal samples using DNA isolation kits, and the DNA will be utilized for the amplification of the V4 variable region of the 16S rRNA gene using 515F/806R primers.Task 2.2. Metabolomicsanalysisusing mouse serum and feces: Global biochemical profiles of the serum and feces from mice will be performed by Metabolon (Durham, NC) using the Ultrahigh Performance Liquid Chromatography-Tandem Mass Spectroscopy platforms. The data will be extracted, and then peaks and compounds will be identified by the LAN backbone and database server running Oracle Enterprise Edition. Data will be further analyzed by principal components analysis and random forest. Significantly changed top biochemicals will be further used to determine the possible metabolic pathways using the online tool MetaboAnalyst 3.0.Task 2.3 Pathological analyses of inflammation and intestinal barrier integrity in the colon:The formalin-fixed small intestine and colon (ileum and rectal section) of mice will be sectioned and stained with hematoxylin and eosin. The slides will be blinded/coded, and the colon epithelial architecture and inflammation will be histologically scored using the following system: 0, normal; 1, increased number of inflammatory cells in lamina propria; 2, increased number of inflammatory cells in submucosa; 3, dense inflammatory cell mass, but not transmural in nature; 4, transmural inflammation as we and others reported34. Intestinal epithelial architecture will be scored on architectural changes: 0, normal; 1, blebbing; 2, loss of epithelium; 3, complete loss of crypt architecture.Stained slides will be analyzed by two pathologists in a blinded manner.Objective 3.Pre-clinical investigation of the correlation between different types of dietary fiber from foods and gut microbiome among underrepresented populations in the Southern Community Cohort Study;Study Population and Methods:The Southern Community Cohort Study (SCCS) is a prospective cohort study designed to investigate health disparities in the US, particularly among African Americans.The dietary fiber type/quantity data will be generated by linking the FFQ data with the type and content of fiber of interest in each food. and correlate to the gut microbiome information. Results generated from this study will be compared with the results obtained from in vitro and in vivo tests.The study contains three major tasks: 1) access to the SCCS data, 2) extract dietary fiber information and quantify the fiber type of interest, and 3) conduct a correlation study on dietary fiber and gut microbiota.Task 3.1.Submit a Data Use Proposal to the Southern Community Cohort Study and Obtain dietary and gut microbiome data: We will submit a proposal tothe SCCS committee to obtain data needed for the research. The proposal includes an abstract, background, hypothesis, specific aims, analytic plan, timeline, references, and exact SCCS variable names (e.g., FFQ variables and gut microbiome diversity and taxa).Task 3.2. Identify dietary fiber type and quantity from the Food Frequency Questionnaire:We will link intakes of each food item in the FFQ with the fiber type and content in each food to generate dietary fiber type/quantity data in the SCCS. A re intakes of total and different types of dietary fiber by age group (e.g., older or younger than 60 years), sex (male or female), race (Black, White, and others), and health status (e.g., history of diabetes, hypertension, cardiovascular diseases, cancer, and digestive diseases) usingANOVA.Task 3.3.Examine the correlation of dietary fiber intakes with gut microbiome profile: We willevaluate the intakes of total and different types of dietary fiber in relation to gut microbiome diversity and taxa relative abundance among ~600 Black participants in the SCCS with existing shotgun metagenomics data. Participants who reported antibiotic use, diarrhea, or bowel preparation in the past 2 months will be excluded from the analysis. Microbiome taxonomy will be assigned using MetaPhlAn3. Taxa relative abundance will be centered log-ratio transformed with a pseudo count of 0.5. Alpha-diversity will be assessed by Shannon Index and β-diversity by Bray-Curtis distances at the species level. Generalized linear regression models will be used to evaluate the associations of total and different types of dietary fiber with microbiome α-diversity and taxa relative abundance. Covariates will include enrollment age, time interval between enrollment and stool collection, sex, education, income, smoking status, physical activity, and total energy intake inModel 1; plus body mass index and disease history (e.g., diabetes, hypertension, cardiovascular disease, and cancer) inModel 2.PERMANOVA with 999 permutations will be used to evaluate dietary fiber in relation to microbiome β-diversity. A violin plot, principle coordinates analysis plot, and phylogenetic tree will be used to visualize results. Benjamini and Hochberg false discovery rates (FDR) <0.10 will be considered significant. We will conduct stratified analysis by age group, sex, and health status to evaluate if those factors may modify the fiber-microbiome relationship. The analysis will be limited to SCCS participants with valid FFQ data and reported plausible total energy intake.